Journal of Data Science and Its Applications
JDSA welcomes all topics that are relevant to data science, computational linguistics, and information sciences. The listed topics of interest are as follows: Big Data Analytics Computational Linguistics Data Clustering and Classifications Data Mining and Data Analytics Data Visualization Information Science Tools and Applications in Data Science
Articles
30 Documents
<![CDATA[Transition Strategies of Change Management For the Succesful Implementation of Data Warehouse of Higher Education in Indonesia ]]>
<![CDATA[Ade Rahmat Iskandar]]>;
<![CDATA[Ari Purno]]>
<![CDATA[ Journal of Data Science and Its Applications Vol 1 No 1 (2018): Journal of Data Science and Its Applications ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/jdsa.2018.1.2
<![CDATA[Data Warehouse of Higher Education offers enormous advantages in efficiency, productivity, cost reduction and information integration system. This paper conducted to support success transition of implementation of Data Warehouse of Higher Education in Indonesia (that is called ‘Sistem Pangkalan Data Pendidikan Tinggi or PD-DIKTI in Indonesian) that had been implemented since 2013 ago. However, Data Warehouse of Higher Education implementations are complex, with many encountering difficulty and even failure. Transition of Data Warehouse implementation has been identified as critical success factor. A model that is used to manage the transition of Data Warehouse of Higher Education is Bridge’s Model, where this model is included to top ten leading transition for managing change in the world. The paper is going to summerize the results of a selected relevant articles both of the success implementation new IT technology and how to handle resistance to make transition hoped. In addition, we need to look at the transition models. The paper is hopefully able in changing the better equipped management to get satisfactory decision for all involved in implementing new IT system. This paper also conducted with Action research to get the information trusted.]]>
<![CDATA[Mapping Organization Knowledge Network and Social Media Based Reputation Management ]]>
<![CDATA[Andry Alamsyah]]>;
<![CDATA[Maribella Syawiluna]]>
<![CDATA[ Journal of Data Science and Its Applications Vol 1 No 1 (2018): Journal of Data Science and Its Applications ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/jdsa.2018.1.3
<![CDATA[Knowledge management are important aspects in an organization, especially in ICT industry. Having more control of it is essentials for the organization to stay competitive in the business. One way to assess the organization knowledge capital is by measuring employee knowledge network and their personal reputation in social media. Using this measurement, we see how employee build relationship around their peer networks or clients virtually. We also able to see how knowledge network support organization performance. The research objective is to map knowledge network and reputation formulation in order to fully understand how knowledge flow and whether employee reputation have higher degree of influence in organization knowledge network. We particularly develop formulas to measure knowledge network and personal reputation based on their social media activities. As case study, we pick an Indonesian ICT company which actively build their business around their employee peer knowledge outside the company. For knowledge network, we perform data collection by conducting interviews. For reputation management, we collect data from several popular social media. We base our work on Social Network Analysis (SNA) methodology. The result shows that employees knowledge is directly proportional with their reputation, but there are different reputations level on different social media observed in this research.]]>
<![CDATA[Ensemble Based Gustafson Kessel Fuzzy Clustering ]]>
<![CDATA[Achmad Fauzi Bagus Firmansyah]]>;
<![CDATA[Setia Pramana]]>
<![CDATA[ Journal of Data Science and Its Applications Vol 1 No 1 (2018): Journal of Data Science and Its Applications ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/jdsa.2018.1.6
<![CDATA[Fuzzy clustering is a clustering method whcih allows an object to belong to two or more cluster by combining hard-clustering and fuzzy membership matrix. Two popular algorithms used in fuzzy clustering are Fuzzy C-Means (FCM) and Gustafson Kessel (GK). The FCM use Euclideans distance for determining cluster membership, while GK use Fuzzy Covariance Matrix that considering covariance between variables. Although GK perform better, it has some drawbacks on handling linearly correlated data, and as FCM the algorithm produce unstable result due to random initialization. These drawbacks can be overcame by using improved covariance estimation and cluster ensemble, respectively. This research presents the implementation of improved covariance estimation and cluster ensemble on GK method and compare it with FCM-Ensemble.]]>
<![CDATA[Designing Interface of Mobile Parental Information System based on Users’ Perception Using Kansei Engingeering ]]>
<![CDATA[Ana Hadiana]]>
<![CDATA[ Journal of Data Science and Its Applications Vol 1 No 1 (2018): Journal of Data Science and Its Applications ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/jdsa.2018.1.7
<![CDATA[Users’ psychological emotion plays important role in designing an interface of software including application of information system. This research attempted to implement Kansei Engineering Type I (KEPack) as a method to analyze kinds of emotional factor related to user interface for mobile Parental Information System. This research used Kansei Words to explore users’ requirements based on psychological factors. Eighteen words were used for Kansei Words that have relationship with Parental Information System. Ten samples of mobile information system were selected as specimens considered suitable for designing interface of Parental Information System. Data questionnaires collected from thirty respondents were processed using multivariate statistical analysis such as Factor Analysis (FA) and Partial Least Square (PLS). This research found that the two important emotional factors i.e funny and informative have to be considered for designing user interface for mobile Parental Information System.]]>
<![CDATA[Classifying Electronic Word of Mouth and Competitive Position in Online Game Industry ]]>
<![CDATA[Bram Manuel]]>;
<![CDATA[Dodie Tricahyono]]>
<![CDATA[ Journal of Data Science and Its Applications Vol 1 No 1 (2018): Journal of Data Science and Its Applications ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/jdsa.2018.1.9
<![CDATA[The number of online review in online game industry growing significantly along with growing rateof internet adoption. With abundant number of data, one can acquire limitless insight, for example,information regarding of electronic word-of-mouth (e-WOM) whom greatly affecting consumerbehavior and business performance. Knowledge of e-WOM can be used as competitive intelligenceto deal with industrial competition. Therefore, this research answers how to classify e-WOM, whatare e-WOM aspects emerge in MMOFPS game, and how does comparison of e-WOM positivitybetween the three MMOFPS Game used as research objects. Dataset are constructed from Reviewpage of Steam website for respective games with total 499 reviews used as sample data. Then theanalysis conducted using Orange and Indico API as tools. Therefore, we found several noun wordsfrequently used as opinion target and we also found out that in aspect-level comparison, Game 2gain the highest e-WOM positivity value in community aspect and Game 1 gain the highest e-WOMpositivity value in general aspect. Thus, each respective game developer can manage to furtherdevelop their strategies from the information of their competitive position in the industry]]>
<![CDATA[Classification of Electrocardiogram Signals using Principal Component Analysis and Levenberg Marquardt Backpropagation for Detection Ventricular Tachyarrhythmia ]]>
<![CDATA[Astrima Manik]]>;
<![CDATA[Adiwijaya Adiwijaya]]>;
<![CDATA[Dody Qori Utama]]>
<![CDATA[ Journal of Data Science and Its Applications Vol 2 No 1 (2019): Journal of Data Science and Its Applications ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/jdsa.2019.2.12
<![CDATA[Abstract Ventricular Tachyarrhythmia (VT) are the primary arrhythmias which are cause of sudden death. For someone who already has symptoms of VT should immediately perform an examination of one of them by using an electrocardiogram (ECG). An electrocardiogram is a recording of the heart's electrical results in a waveform. However, limited ability in analysis and diagnosis of ECG reading is still difficult to do. Therefore, the classification of ECG signals is needed to detect a person, especially those with VT or not. In this research focuses on the classification of VT heartbeats from ECG signals by using median filter method in preprocessing, Principal Component Analysis (PCA) as feature extraction and modified Backpropagation (MBP) as classification. This research used machine learning method that is a neural network with backpropagation modification that is Levenberg Marquardt to speed up network training process. The best VT detection performance results were based on the average accuracy of the overall scheme of 91.67% with the best parameters that principal component=10 and 20, hidden neuron=4, and µ value=0.001 as well training time 1 seconds with a comparison of train data and test data that is 80:20 percent. Keywords: Electrocardiogram, Levenberg Marquardt Backpropagation, Median filter, Principal Component Analysis, and Ventricular Tachyarrhythmia]]>
<![CDATA[Geo-additive Models in Small Area Estimation of Poverty ]]>
<![CDATA[Novi Hidayat Pusponegoro]]>;
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Anwar Fitrianto]]>;
<![CDATA[I Made Sumertajaya]]>
<![CDATA[ Journal of Data Science and Its Applications Vol 2 No 1 (2019): Journal of Data Science and Its Applications ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/jdsa.2019.2.15
<![CDATA[Spatial data contains of observation and region information, it can describe spatial patterns such as disease distribution, reproductive outcome and poverty. The main flaw in direct estimation especially in poverty research is the sample adequacy fulfilment otherwise it will produce large estimate parameter variant. The Small Area Estimation (SAE) developed to handle that flaw. Since, the small area estimation techniques require “borrow strength” across the neighbor areas thus SAE was developed by integrating spatial information into the model, named as Spatial SAE. SAE and spatial SAE model require the fulfilment of covariate linearity assumption as well as the normality of the response distribution that is sometimes violated, and the geo-additive model offers to handle that violation using the smoothing function. Therefore, the purpose of this paper is to compare the SAE, Spatial SAE and Geo-additive model in order to estimate at sub-district level mean of per capita income of each area using the poverty survey data in Bangka Belitung province at 2017 by Polytechnic of Statistics STIS. The findings of the paper are the Geo-additive is the best fit model based on AIC, and spatial information don't influence the estimation in SAE and spatial SAE model since they have the similar estimation performance.]]>
<![CDATA[Soccer Players Detection Using GDLS Optimization and Spatial Bitwise Operation Filter ]]>
<![CDATA[Adhi Dharma Wibawa]]>;
<![CDATA[Atyanta Nika Rumaksari]]>
<![CDATA[ Journal of Data Science and Its Applications Vol 2 No 1 (2019): Journal of Data Science and Its Applications ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/jdsa.2019.2.18
<![CDATA[Advancement computer vision technology in order to help coach creates strategy has been affecting the sport industry evolving very fast. Players movement patterns and other important behavioral activities regarding the tactics during playing the game are the most important data obtained in applying computer vision in Sport Industry. The basic technique for extracting those information during the game is player detection. Three fundamental challenges of computer vision in detecting objects are random object’s movement, noise and shadow. Background subtraction is an object’s detection method that used widely for separating moving object as foreground and non moving object as background. This paper proposed a method for removing shadow and unwanted noise by improving traditional background subtraction technique. First, we employed GDLS algorithm to optimize background-foreground separation. Then, we did filter shadows and crumbs-like object pixels by applying digital spatial filter which is created from implementation of digital arithmetic algorithm (bitwise operation). Finally, our experimental result demonstrated that our algorithm outperform conventional background subtraction algorithms. The experiments result proposed method has obtained 80.5% of F1-score with average 20 objects were detected out of 24 objects.]]>
<![CDATA[Analysis Characteristics of Car Sales In E-Commerce Data Using Clustering Model ]]>
<![CDATA[Puspita Kencana Sari]]>;
<![CDATA[Adelia Purwadinata]]>
<![CDATA[ Journal of Data Science and Its Applications Vol 2 No 1 (2019): Journal of Data Science and Its Applications ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/jdsa.2019.2.19
<![CDATA[The number of car sales in e-commerce is currently increase along with the increasing use of the Internet in Indonesia. Purchases of Car in Indonesia are currently get higher, especially in used cars, which are a necessity for the community based on the odd-even system of car traffic policies currently applied in Jakarta. This research aims to study characteristics of clusters formed in e-commerce site to predict how are the car sales segmentation. Data is collected from big-two e-commerce site about car selling and buying in Indonesia. Clustering model is build using K-Means method and Davies Bouldin Index as evaluation of the clusters formed. The results show for both clusters, the first cluster has characteristic lowers sale price and older production year. The second cluster has higher price with latest production. From the model performance, evaluation from Davies Bouldin Index is quite good for both models. Keywords : Big Data, Clustering, K-Means, E-Commerce]]>
<![CDATA[Sentiment Analysis of Cyberbullying on Instagram User Comments ]]>
<![CDATA[Muhammad Zidny Naf'an]]>;
<![CDATA[Alhamda Adisoka Bimantara]]>;
<![CDATA[Afiatari Larasati]]>;
<![CDATA[Ezar Mega Risondang]]>;
<![CDATA[Novanda Alim Setya Nugraha]]>
<![CDATA[ Journal of Data Science and Its Applications Vol 2 No 1 (2019): Journal of Data Science and Its Applications ]]>
Publisher : <![CDATA[Telkom University ]]>
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DOI: 10.21108/jdsa.2019.2.20
<![CDATA[Instagram is a social media for sharing images, photos and videos. Instagram has many active users from various circles. In addition to sharing submissions, Instagram users can also give likes and comments to other users' posts. However, the comment feature is often misused, for example it is used for cyberbullying which includes one act against the law. But until now, Instagram still does not provide a feature to detect cyberbullying. Therefore, this study aims to create a system that can classify comments whether they contain elements of cyberbullying or not. The results of the classification will be used to detect cyberbullying comments. The algorithm used for classification is Naïve Bayes Classifier. Then for each comment will pass the preprocessing and feature extraction stages with the TF-IDF method. For evaluation and testing using the K-Fold Cross Validation method. The experiment is divided into two, namely using stemming and without stemming. The training data used is 455 data. The best experimental results obtained an accuracy of 84% both with stemming, and without stemming.]]>
